Natural language processing has come a long way since its foundations were laid in the 1940s and 50s (for an introduction see, e.g., Jurafsky and Martin (2008): Speech and Language Processing, Pearson Prentice Hall). This CRAN task view collects relevant R packages that support computational linguists in conducting analysis of speech and language on a variety of levels - setting focus on words, syntax, semantics, and pragmatics.

In recent years, we have elaborated a framework to be used in
packages dealing with the processing of written material: the package
tm.
Extension packages in this area are highly recommended to interface with tm's basic routines
and useRs are cordially invited to join in the discussion on further developments of this
framework package. To get into natural language processing, the
cRunch service
and
tutorials
may be helpful.

RcmdrPlugin.temis
is an Rcommander plug-in providing an integrated solution to perform a series of text mining tasks such as importing and cleaning a corpus, and analyses like terms and documents counts, vocabulary tables, terms co-occurrences and documents similarity measures, time series analysis, correspondence analysis and hierarchical clustering.

openNLP
provides an R interface to
OpenNLP
, a collection of natural language processing tools including a sentence detector, tokenizer, pos-tagger, shallow and full syntactic parser, and named-entity detector, using the Maxent Java package for training and using maximum entropy models.

Trained models for English and Spanish to be used with
openNLP
are available from
http://datacube.wu.ac.at/
as packages openNLPmodels.en and openNLPmodels.es, respectively.

RWeka
is a interface to
Weka
which is a collection of machine learning algorithms for data mining tasks written in Java. Especially useful in the context of natural language processing is its functionality for tokenization and stemming.

tidytext
provides means for text mining for word processing and sentiment analysis using dplyr, ggplot2, and other tidy tools.

R's base package already provides a rich set of character manipulation routines. See
help.search(keyword = "character", package = "base")
for more information on these capabilities.

wordnet
provides an R interface to
WordNet
, a large lexical database of English.

RKEA
provides an R interface to
KEA
(Version 5.0). KEA (for Keyphrase Extraction Algorithm) allows for extracting keyphrases from text documents. It can be either used for free indexing or for indexing with a controlled vocabulary.

gsubfn
can be used for certain parsing tasks such as extracting words from strings by content rather than by delimiters.
demo("gsubfn-gries")
shows an example of this in a natural language processing context.

textreuse
provides a set of tools for measuring similarity among documents and helps with detecting passages which have been reused. The package implements shingled n-gram, skip n-gram, and other tokenizers; similarity/dissimilarity functions; pairwise comparisons; minhash and locality sensitive hashing algorithms; and a version of the Smith-Waterman local alignment algorithm suitable for natural language.

boilerpipeR
helps with the extraction and sanitizing of text content from HTML files: removal of ads, sidebars, and headers using the boilerpipe Java library.

tau
contains basic string manipulation and analysis routines needed in text processing such as dealing with character encoding, language, pattern counting, and tokenization.

SnowballC
provides exactly the same API as Rstem, but uses a slightly different design of the C libstemmer library from the Snowball project. It also supports two more languages.

stringi
provides R language wrappers to the International Components for Unicode (ICU) library and allows for: conversion of text encodings, string searching and collation in any locale, Unicode normalization of text, handling texts with mixed reading direction (e.g., left to right and right to left), and text boundary analysis (for tokenizing on different aggregation levels or to identify suitable line wrapping locations).

stringdist
implements an approximate string matching version of R's native 'match' function. It can calculate various string distances based on edits (Damerau-Levenshtein, Hamming, Levenshtein, optimal string alignment), qgrams (q-gram, cosine, jaccard distance) or heuristic metrics (Jaro, Jaro-Winkler). An implementation of soundex is provided as well. Distances can be computed between character vectors while taking proper care of encoding or between integer vectors representing generic sequences.

Rstem
(available from Omegahat) is an alternative interface to a C version of Porter's word stemming algorithm.

KoNLP
provides a collection of conversion routines (e.g. Hangul to Jamos), stemming, and part of speech tagging through interfacing with the Lucene's HanNanum analyzer. In version 0.0-8.0, the documentation is sparse and still needs some help.

alineR
helps calculate the phonetic distance between words (the 'ALINE' distance). The score is based on phonetic featuers represented with the Unicode-compliant International Phonetic Alphabet (IPA). Parameterized features weights are used to determine the optimal alignment and functions are provided to estimate optimum values using a genetic algorithm and supervised learning.

ore
provides an alternative to R's built-in functionality for handling regular expressions, based on the Onigmo Regular Expression Library. Offers first-class compiled regex objects, partial matching and function-based substitutions, amongst other features. A benchmark comparing results for ore functions with stringi and the R base implementation is available
regex-performance.

languageR
provides data sets and functions exemplifying statistical methods, and some facilitatory utility functions used in the book by R. H. Baayen: "Analyzing Linguistic Data: a Practical Introduction to Statistics Using R", Cambridge University Press, 2008.

zipfR
offers some statistical models for word frequency distributions. The utilities include functions for loading, manipulating and visualizing word frequency data and vocabulary growth curves. The package also implements several statistical models for the distribution of word frequencies in a population. (The name of this library derives from the most famous word frequency distribution, Zipf's law.)

maxent
is an implementation of maxinum entropy minimising memory consumption of very large data-sets.

wordcloud
provides a visualisation similar to the famous wordle ones: it horizontally and vertically distributes features in a pleasing visualisation with the font size scaled by frequency.

hunspell
is a stemmer and spell-checker library designed for languages with rich morphology and complex word compounding or character encoding. The package can check and analyze individual words as well as search for incorrect words within a text, latex or (R package) manual document.

tesseract
is an OCR engine with unicode (UTF-8) support that can recognize over 100 languages out of the box.

mscsweblm4r
provides an interface to the Microsoft Cognitive Services Web Language Model API and can be used to calculate the probability for a sequence of words to appear together, the conditional probability that a specific word will follow an existing sequence of words, get the list of words (completions) most likely to follow a given sequence of words, and insert spaces into a string of words adjoined together without any spaces (hashtags, URLs, etc.).

mscstexta4r
provides an interface to the Microsoft Cognitive Services Text Analytics API and can be used to perform sentiment analysis, topic detection, language detection, and key phrase extraction.

Semantics:

lsa
provides routines for performing a latent semantic analysis with R. The basic idea of latent semantic analysis (LSA) is, that text do have a higher order (=latent semantic) structure which, however, is obscured by word usage (e.g. through the use of synonyms or polysemy). By using conceptual indices that are derived statistically via a truncated singular value decomposition (a two-mode factor analysis) over a given document-term matrix, this variability problem can be overcome. The article
Investigating Unstructured Texts with Latent Semantic Analysis
gives a detailed overview and demonstrates the use of the package with examples from the are of technology-enhanced learning.

topicmodels
provides an interface to the C code for Latent Dirichlet Allocation (LDA) models and Correlated Topics Models (CTM) by David M. Blei and co-authors and the C++ code for fitting LDA models using Gibbs sampling by Xuan-Hieu Phan and co-authors.

lda
implements Latent Dirichlet Allocation and related models similar to LSA and topicmodels.

stm
(Structural Topic Model) implements a topic model derivate that can include document-level meta-data. The package also includes tools for model selection, visualization, and estimation of topic-covariate regressions.

kernlab
allows to create and compute with string kernels, like full string, spectrum, or bounded range string kernels. It can directly use the document format used by
tm
as input.